Journal: PLoS ONE

Article Title: Comparing the reliability of different ICA algorithms for fMRI analysis

doi: 10.1371/journal.pone.0270556

Figure Lengend Snippet: Repetition number k did not influence Iq values for non-deterministic ICA (sensory data).

Article Snippet: For both sensory data and motor data, only the results of FastICA exhibited good spatial consistency with the results of Infomax when considering median and minimum SCC values ( Tables and ).

Techniques:

Journal: PLoS ONE

Article Title: Comparing the reliability of different ICA algorithms for fMRI analysis

doi: 10.1371/journal.pone.0270556

Figure Lengend Snippet: ICASSO repetition number k with the highest median Iq of each algorithm (sensory data).

Article Snippet: For both sensory data and motor data, only the results of FastICA exhibited good spatial consistency with the results of Infomax when considering median and minimum SCC values ( Tables and ).

Techniques:

Journal: PLoS ONE

Article Title: Comparing the reliability of different ICA algorithms for fMRI analysis

doi: 10.1371/journal.pone.0270556

Figure Lengend Snippet: Repetition number k did not influence Iq values for non-deterministic ICA (motor data).

Article Snippet: For both sensory data and motor data, only the results of FastICA exhibited good spatial consistency with the results of Infomax when considering median and minimum SCC values ( Tables and ).

Techniques:

Journal: PLoS ONE

Article Title: Comparing the reliability of different ICA algorithms for fMRI analysis

doi: 10.1371/journal.pone.0270556

Figure Lengend Snippet: ICASSO repetition number k with the highest median Iq of each algorithm (motor data).

Article Snippet: For both sensory data and motor data, only the results of FastICA exhibited good spatial consistency with the results of Infomax when considering median and minimum SCC values ( Tables and ).

Techniques:

Journal: PLoS ONE

Article Title: Comparing the reliability of different ICA algorithms for fMRI analysis

doi: 10.1371/journal.pone.0270556

Figure Lengend Snippet: Differences in SCC values between the most reliable ICASSO results and the other nine results (sensory data).

Article Snippet: For both sensory data and motor data, only the results of FastICA exhibited good spatial consistency with the results of Infomax when considering median and minimum SCC values ( Tables and ).

Techniques:

Journal: PLoS ONE

Article Title: Comparing the reliability of different ICA algorithms for fMRI analysis

doi: 10.1371/journal.pone.0270556

Figure Lengend Snippet: Differences in SCC values between the most reliable ICASSO results and the other nine results (motor data).

Article Snippet: For both sensory data and motor data, only the results of FastICA exhibited good spatial consistency with the results of Infomax when considering median and minimum SCC values ( Tables and ).

Techniques:

Journal: PLoS ONE

Article Title: Comparing the reliability of different ICA algorithms for fMRI analysis

doi: 10.1371/journal.pone.0270556

Figure Lengend Snippet: Range of SCC values between the most reliable ICASSO results and the other nine results (sensory data).

Article Snippet: For both sensory data and motor data, only the results of FastICA exhibited good spatial consistency with the results of Infomax when considering median and minimum SCC values ( Tables and ).

Techniques:

Journal: PLoS ONE

Article Title: Comparing the reliability of different ICA algorithms for fMRI analysis

doi: 10.1371/journal.pone.0270556

Figure Lengend Snippet: Range of SCC values between the most reliable ICASSO results and the other nine results (motor data).

Article Snippet: For both sensory data and motor data, only the results of FastICA exhibited good spatial consistency with the results of Infomax when considering median and minimum SCC values ( Tables and ).

Techniques:

Journal: PLoS ONE

Article Title: Comparing the reliability of different ICA algorithms for fMRI analysis

doi: 10.1371/journal.pone.0270556

Figure Lengend Snippet: SCC values between the most reliable Infomax results and the other nine results (sensory data).

Article Snippet: For both sensory data and motor data, only the results of FastICA exhibited good spatial consistency with the results of Infomax when considering median and minimum SCC values ( Tables and ).

Techniques:

Journal: PLoS ONE

Article Title: Comparing the reliability of different ICA algorithms for fMRI analysis

doi: 10.1371/journal.pone.0270556

Figure Lengend Snippet: SCC values between the most reliable Infomax results and the other nine results (motor data).

Article Snippet: For both sensory data and motor data, only the results of FastICA exhibited good spatial consistency with the results of Infomax when considering median and minimum SCC values ( Tables and ).

Techniques:

Journal: Computational Intelligence and Neuroscience

Article Title: PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

doi: 10.1155/2016/9754813

Figure Lengend Snippet: Comparison of Amari indices for different BSS approaches trained on randomly mixed VAR simulated data. The algorithms with average Amari indices significantly lower ( p < 0.05) than AMICA, Extended Infomax, and FastICA are bold, with the largest p value for the three comparisons shown in parentheses. The cells that are not bold had Amari index distributions that were not significantly lower ( p < 0.05) than at least one of the algorithms AMICA, Extended Infomax, and FastICA. The average baseline was computed by generating 50,000 random demixing matrices for each of the 20 mixing matrices, resulting in a distribution of one million Amari indices, the average of which is reported.

Article Snippet: More importantly, FastICA, Infomax ICA, AMICA, and the proposed Hilbert approach find many components with good dipole RV values but that do not correlate well with a source and/or are not physically close to a source they correlate well with.

Techniques: Comparison

Journal: Computational Intelligence and Neuroscience

Article Title: PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

doi: 10.1155/2016/9754813

Figure Lengend Snippet: Average number of sources that are matched by a demixing solution component with absolute correlation ≥ 0.7 per experiment. Recall that Experiments 1 and 3 had 10 sources and Experiment 2 was generated by 5 sources. Bold values correspond to the significant PWC-ICA algorithms (by the Amari index) as described in Table 1 .

Article Snippet: More importantly, FastICA, Infomax ICA, AMICA, and the proposed Hilbert approach find many components with good dipole RV values but that do not correlate well with a source and/or are not physically close to a source they correlate well with.

Techniques: Generated

Journal: BioMed Research International

Article Title: A Review of Feature Extraction Software for Microarray Gene Expression Data

doi: 10.1155/2014/213656

Figure Lengend Snippet: Summary of ICA software.

Article Snippet: 3 , HiPerSAT , Keith et al. [ ] , C++, MATLAB, and EEGLAB , (i) Integration of FastICA, Informax, and SOBI algorithms (ii) Data whitening is provided.

Techniques: Software, Extraction

Journal: PLoS ONE

Article Title: Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

doi: 10.1371/journal.pone.0073309

Figure Lengend Snippet: A Gaussian has zero excess kurtosis. Here as in Example 2 of the original paper . The four vertical lines at correspond to the relative sizes of the small box, the medium box, the large box, and a very large box corresponding to the maximal kurtosis case. Note that the medium and large box experiments have near zero excess kurtosis, i.e., kurtosis value matching that of a Gaussian . In addition, the pdfs of these sources are bimodal (see inset figures), ensuring that ICA algorithms designed for unimodal super-Gaussian distributions such as Infomax and FastICA with standard parameter settings, will likely fail. At the bottom of the figure are the ISI values (see Equation (2)) for the various algorithms at those four points (see for full list). Also note the best separation performance of Infomax and FastICA for the maximum kurtosis case, which corresponds to almost the lowest level of sparsity.

Article Snippet: Summary : FastICA, Infomax (super), and ICA-EBM perform well , , , , .

Techniques:

Journal: PLoS ONE

Article Title: Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

doi: 10.1371/journal.pone.0073309

Figure Lengend Snippet: Source estimates for the four cases indicated in Figure 1 .

Article Snippet: Summary : FastICA, Infomax (super), and ICA-EBM perform well , , , , .

Techniques:

Journal: PLoS ONE

Article Title: Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

doi: 10.1371/journal.pone.0073309

Figure Lengend Snippet: Tabulated results for the so-called [8] ICA “promotional material.”

Article Snippet: Summary : FastICA, Infomax (super), and ICA-EBM perform well , , , , .

Techniques:

Journal: Cell

Article Title: Cerebellar neurodynamics predict decision timing and outcome on single-trial level

doi: 10.1016/j.cell.2019.12.018

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: ​ REAGENT or RESOURCE SOURCE IDENTIFIER Experimental Models: Organisms/Strains Tg( elavl3:H2B-GCaMP6s ) ( Vladimirov et al., 2014 ) N/A Tg(gSA2AzGFF152B) ( Takeuchi et al., 2015 ) N/A Tg(Huc:H2B-GCaMP6s; GAD1b:RFP) ( Freeman et al., 2014 ; Satou et al, 2013 ) N/A Tg(UAS:GCaMP6s) ( Filosa et al, 2016 ) N/A Software and Algorithms μManager Open Imaging https://micro-manager.org/ MATLAB (Behavioral and imaging analysis, modeling) MathWorks https://www.mathworks.com/products/matlab.html FIJI (ImageJ) (Image Processing) NIH http://fiji.sc Custom data-processing pipeline for LFM data ( Prevedel et al, 2014 ) https://static-content.springer.com/esm/art%3A10.1038%2Fnmeth.2964/MediaObjects/41592_2014_BFnmeth2964_MOESM189_ESM.zip Demixed Principal Component Analysis (dPCA) ( Kobak et al, 2016 ) https://github.com/machenslab/dPCA t-Distributed Stochastic Neighbor Embedding (t-SNE) ( Maaten and Hinton, 2008 ) https://lvdmaaten.github.io/tsne/ Independent Component Analysis (FastICA) ( Hyvarinen and Oja, 2000 ) https://research.ics.aalto.fi/ica/fastica/ Scanlmage ( Pologruto et al, 2003 ) http://scanimage.vidriotechnologies.com/display/SIH/ScanImage+Home CalmAn-MATLAB ( Giovannucci et al, 2019 ) https://github.com/flatironinstitute/CaImAn-MATLAB DeepLabCut 2.0 ( Nath et al, 2019 ) https://github.com/AlexEMG/DeepLabCut Open in a separate window KEY RESOURCES TABLE Whole-brain Ca 2+ imaging and decision making in larval zebrafish on the single-trial level Pre-motor bifurcation and trial-by-trial changes of functional connectivity Cerebellar activity predicts decision outcome and decision time >10 s before movement Hemisphere difference encodes decision; joint hemisphere activity encodes timing

Techniques: Software, Imaging

Journal: Journal of Pharmaceutical Health Care and Sciences

Article Title: A laboratory data-based evaluation of the efficacy and safety of generic pravastatin sodium for long-term use

doi: 10.1186/s40780-015-0033-4

Figure Lengend Snippet: Cases of adverse reactions that have not been reported for the branded drug

Article Snippet: The use of the concomitant drugs Olmetec® (Daiichi Sankyo Co., Ltd.) and Fastic® tablet (Mochida Pharmaceutical Co., Ltd.) was not discontinued (Table ).

Techniques: